With the rapid development of data analysis science, the technology of data analysis has become mature gradually. Therefore, various kinds of data are collected and analyzed to provide relevant reference information for various uses. In recent years, the construction of smart electricity meters has become the mainstream, and the smart electricity meters can report total power consumption of users via networks. Therefore, a problem being considered in the art is how to analyze the power consumption of user appliances to identify a use status of the user appliances in a certain period of time based on the total power consumption of the user, and establish a power consumption analyzing model for the user to further provide diversified applications according to the data being collected, e.g., provide a suggestion for the power consumption habit and behavior of the user according to the power consumption analyzing model.
Conventional user appliance power consumption analyzing technology collects total power consumption data (e.g., total power) of a user to further estimate the power consumption of main appliances through a non-intrusive electricity load monitoring technology. However, in order to obtain accurate power consumption feature values of the appliances, the conventional non-intrusive electricity load monitoring technology needs to obtain the total power consumption data of the user at a high sampling frequency (which is generally greater than 1 Hz), and then manual intervention is required to perform analysis and judgment to distinguish and label the power consumption feature values of various appliances from the total power consumption data of the user to generate labeled data. Therefore, the cost of establishing the power consumption analyzing model is very high, especially in the case where most of smart electricity meters are not capable of obtaining the total power consumption data of the user at a high sampling frequency.
Moreover, different users may be under different environments, so service providers may need to re-obtain the total power consumption data of the users to establish different power consumption analyzing models to overcome the environmental factors in order to ensure that the power consumption analyzing model can suit each of the users. Otherwise, the problem of overfitting is very likely to occur (i.e., excessive parameters are used when establishing the power consumption analyzing model) by applying the same power consumption analyzing model to users under different environments. Therefore, the cost of establishing the power consumption analyzing models will be further increased remarkably by establishing a power consumption analyzing model for each of the users in order to provide reliable power consumption analyzing service for the users.
Accordingly, an urgent need exists in the art to provide a power consumption analyzing mechanism, which can label the power consumption feature values of various appliances from the total power consumption data of the user without the need of obtaining the total power consumption data of the user at a high sampling rate and without the need of manual intervention for analyzing the total power consumption data of the user, thereby reducing the cost of establishing the power consumption analyzing model.